Adaptive Detection of Range-Distributed Targets Based on SAR Raw Data

Inhalt:
Conventionally, Synthetic Aperture Radar (SAR) Automatic Target Detection and Recognition (ATD/R) are often performed in SAR image domain. A novel scheme of SAR target detection in the state of non-imaging is presented. More precisely, this paper addresses adaptive detection of SAR possibly range-distributed targets based on range-compressed but azimuth-uncompressed SAR raw data, rather than on the processed SAR image. The SAR target detection is established in the context of space-time adaptive processing (STAP) and the spatial-temporal steering vector of an airborne stripmap SAR is first derived by exploiting signature diversity, namely of the fact that SAR can change the transmitted signal as the azimuth varies. The adaptive modified generalized likelihood ratio test (AMGLRT) proposed by A.De Maio is employed to detect range-distributed target from SAR raw data. Application of the AMGLRT to the recorded live SAR clutter raw data collected by an X band airborne high resolution SAR shows the feasibility of this novel detection scheme in possible realistic SAR scenarios.